LibreChat/api/server/services/Files/images/encode.js
Danny Avila 5f2d1c5dc9
👁️ feat: Azure Mistral OCR Strategy (#7888)
* 👁️ feat: Add Azure Mistral OCR strategy and endpoint integration

This commit introduces a new OCR strategy named 'azure_mistral_ocr', allowing the use of a Mistral OCR endpoint deployed on Azure. The configuration, schemas, and file upload strategies have been updated to support this integration, enabling seamless OCR processing via Azure-hosted Mistral services.

* 🗑️ chore: Clean up .gitignore by removing commented-out uncommon directory name

* chore: remove unused vars

* refactor: Move createAxiosInstance to packages/api/utils and update imports

- Removed the createAxiosInstance function from the config module and relocated it to a new utils module for better organization.
- Updated import paths in relevant files to reflect the new location of createAxiosInstance.
- Added tests for createAxiosInstance to ensure proper functionality and proxy configuration handling.

* chore: move axios helpers to packages/api

- Added logAxiosError function to @librechat/api for centralized error logging.
- Updated imports across various files to use the new logAxiosError function.
- Removed the old axios.js utility file as it is no longer needed.

* chore: Update Jest moduleNameMapper for improved path resolution

- Added a new mapping for '~/' to resolve module paths in Jest configuration, enhancing import handling for the project.

* feat: Implement Mistral OCR API integration in TS

* chore: Update MistralOCR tests based on new imports

* fix: Enhance MistralOCR configuration handling and tests

- Introduced helper functions for resolving configuration values from environment variables or hardcoded settings.
- Updated the uploadMistralOCR and uploadAzureMistralOCR functions to utilize the new configuration resolution logic.
- Improved test cases to ensure correct behavior when mixing environment variables and hardcoded values.
- Mocked file upload and signed URL responses in tests to validate functionality without external dependencies.

* feat: Enhance MistralOCR functionality with improved configuration and error handling

- Introduced helper functions for loading authentication configuration and resolving values from environment variables.
- Updated uploadMistralOCR and uploadAzureMistralOCR functions to utilize the new configuration logic.
- Added utility functions for processing OCR results and creating error messages.
- Improved document type determination and result aggregation for better OCR processing.

* refactor: Reorganize OCR type imports in Mistral CRUD file

- Moved OCRResult, OCRResultPage, and OCRImage imports to a more logical grouping for better readability and maintainability.

* feat: Add file exports to API and create files index

* chore: Update OCR types for enhanced structure and clarity

- Redesigned OCRImage interface to include mandatory fields and improved naming conventions.
- Added PageDimensions interface for better representation of page metrics.
- Updated OCRResultPage to include dimensions and mandatory images array.
- Refined OCRResult to include document annotation and usage information.

* refactor: use TS counterpart of uploadOCR methods

* ci: Update MistralOCR tests to reflect new OCR result structure

* chore: Bump version of @librechat/api to 1.2.3 in package.json and package-lock.json

* chore: Update CONFIG_VERSION to 1.2.8

* chore: remove unused sendEvent function from config module (now imported from '@librechat/api')

* chore: remove MistralOCR service files and tests (now in '@librechat/api')

* ci: update logger import in ModelService tests to use @librechat/data-schemas

---------

Co-authored-by: arthurolivierfortin <arthurolivier.fortin@gmail.com>
2025-06-13 15:14:57 -04:00

221 lines
6.6 KiB
JavaScript

const axios = require('axios');
const { logAxiosError } = require('@librechat/api');
const {
FileSources,
VisionModes,
ImageDetail,
ContentTypes,
EModelEndpoint,
} = require('librechat-data-provider');
const { getStrategyFunctions } = require('~/server/services/Files/strategies');
/**
* Converts a readable stream to a base64 encoded string.
*
* @param {NodeJS.ReadableStream} stream - The readable stream to convert.
* @param {boolean} [destroyStream=true] - Whether to destroy the stream after processing.
* @returns {Promise<string>} - Promise resolving to the base64 encoded content.
*/
async function streamToBase64(stream, destroyStream = true) {
return new Promise((resolve, reject) => {
const chunks = [];
stream.on('data', (chunk) => {
chunks.push(chunk);
});
stream.on('end', () => {
try {
const buffer = Buffer.concat(chunks);
const base64Data = buffer.toString('base64');
chunks.length = 0; // Clear the array
resolve(base64Data);
} catch (err) {
reject(err);
}
});
stream.on('error', (error) => {
chunks.length = 0;
reject(error);
});
}).finally(() => {
// Clean up the stream if required
if (destroyStream && stream.destroy && typeof stream.destroy === 'function') {
stream.destroy();
}
});
}
/**
* Fetches an image from a URL and returns its base64 representation.
*
* @async
* @param {string} url The URL of the image.
* @returns {Promise<string>} The base64-encoded string of the image.
* @throws {Error} If there's an issue fetching the image or encoding it.
*/
async function fetchImageToBase64(url) {
try {
const response = await axios.get(url, {
responseType: 'arraybuffer',
});
const base64Data = Buffer.from(response.data).toString('base64');
response.data = null;
return base64Data;
} catch (error) {
const message = 'Error fetching image to convert to base64';
throw new Error(logAxiosError({ message, error }));
}
}
const base64Only = new Set([
EModelEndpoint.google,
EModelEndpoint.anthropic,
'Ollama',
'ollama',
EModelEndpoint.bedrock,
]);
const blobStorageSources = new Set([FileSources.azure_blob, FileSources.s3]);
/**
* Encodes and formats the given files.
* @param {Express.Request} req - The request object.
* @param {Array<MongoFile>} files - The array of files to encode and format.
* @param {EModelEndpoint} [endpoint] - Optional: The endpoint for the image.
* @param {string} [mode] - Optional: The endpoint mode for the image.
* @returns {Promise<{ text: string; files: MongoFile[]; image_urls: MessageContentImageUrl[] }>} - A promise that resolves to the result object containing the encoded images and file details.
*/
async function encodeAndFormat(req, files, endpoint, mode) {
const promises = [];
/** @type {Record<FileSources, Pick<ReturnType<typeof getStrategyFunctions>, 'prepareImagePayload' | 'getDownloadStream'>>} */
const encodingMethods = {};
/** @type {{ text: string; files: MongoFile[]; image_urls: MessageContentImageUrl[] }} */
const result = {
text: '',
files: [],
image_urls: [],
};
if (!files || !files.length) {
return result;
}
for (let file of files) {
/** @type {FileSources} */
const source = file.source ?? FileSources.local;
if (source === FileSources.text && file.text) {
result.text += `${!result.text ? 'Attached document(s):\n```md' : '\n\n---\n\n'}# "${file.filename}"\n${file.text}\n`;
}
if (!file.height) {
promises.push([file, null]);
continue;
}
if (!encodingMethods[source]) {
const { prepareImagePayload, getDownloadStream } = getStrategyFunctions(source);
if (!prepareImagePayload) {
throw new Error(`Encoding function not implemented for ${source}`);
}
encodingMethods[source] = { prepareImagePayload, getDownloadStream };
}
const preparePayload = encodingMethods[source].prepareImagePayload;
/* We need to fetch the image and convert it to base64 if we are using S3/Azure Blob storage. */
if (blobStorageSources.has(source)) {
try {
const downloadStream = encodingMethods[source].getDownloadStream;
let stream = await downloadStream(req, file.filepath);
let base64Data = await streamToBase64(stream);
stream = null;
promises.push([file, base64Data]);
base64Data = null;
continue;
} catch (error) {
// Error handling code
}
} else if (source !== FileSources.local && base64Only.has(endpoint)) {
const [_file, imageURL] = await preparePayload(req, file);
promises.push([_file, await fetchImageToBase64(imageURL)]);
continue;
}
promises.push(preparePayload(req, file));
}
if (result.text) {
result.text += '\n```';
}
const detail = req.body.imageDetail ?? ImageDetail.auto;
/** @type {Array<[MongoFile, string]>} */
const formattedImages = await Promise.all(promises);
promises.length = 0;
for (const [file, imageContent] of formattedImages) {
const fileMetadata = {
type: file.type,
file_id: file.file_id,
filepath: file.filepath,
filename: file.filename,
embedded: !!file.embedded,
metadata: file.metadata,
};
if (file.height && file.width) {
fileMetadata.height = file.height;
fileMetadata.width = file.width;
}
if (!imageContent) {
result.files.push(fileMetadata);
continue;
}
const imagePart = {
type: ContentTypes.IMAGE_URL,
image_url: {
url: imageContent.startsWith('http')
? imageContent
: `data:${file.type};base64,${imageContent}`,
detail,
},
};
if (mode === VisionModes.agents) {
result.image_urls.push({ ...imagePart });
result.files.push({ ...fileMetadata });
continue;
}
if (endpoint && endpoint === EModelEndpoint.google && mode === VisionModes.generative) {
delete imagePart.image_url;
imagePart.inlineData = {
mimeType: file.type,
data: imageContent,
};
} else if (endpoint && endpoint === EModelEndpoint.google) {
imagePart.image_url = imagePart.image_url.url;
} else if (endpoint && endpoint === EModelEndpoint.anthropic) {
imagePart.type = 'image';
imagePart.source = {
type: 'base64',
media_type: file.type,
data: imageContent,
};
delete imagePart.image_url;
}
result.image_urls.push({ ...imagePart });
result.files.push({ ...fileMetadata });
}
formattedImages.length = 0;
return { ...result };
}
module.exports = {
encodeAndFormat,
};